Business Statistics and Quantitative Analysis - What is your Prediction?

Anonymous
timer Asked: Dec 12th, 2018
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Question description

Topic: What is your Prediction?

A time series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. Here are some examples of variables you can use to forecast. You may use a different source other than the ones listed (be sure to reference the website). There are many other variables you can use, as long as you have values that are recorded at successive intervals of time.

Main Post: Once you have historical data, address the following:

1. State the variable you are forecasting.

2. Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data values.

3. Use the Time Series Forecasting Templates to forecast using moving average, weighted moving average, and exponential smoothing.

4. Copy/paste the results of each method into your post. Be sure to state the number of periods used in the moving average method, the weights used in the weighted moving average, and the value of α used in exponential smoothing.

5. Clearly state the “next period” prediction for each method.

Unit 9 Discussion – What is your Prediction? Unit 9 Discussion Example – Main Post A time series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. Here are some examples of variables you can use to forecast. You may use a different source other than the ones listed (be sure to cite your reference). There are many other variables you can use, as long as you have values that are recorded at successive intervals of time. See the DB Starter video in the Unit 9 LiveBinder. • • • • • • Currency price: XE (http://www.xe.com/currencyconverter/ ) GNP: Trading Economics (http://www.tradingeconomics.com/united-states/gross-national-product ) Average home sales: National Association of Realtors (http://www.realtor.org/topics/existing-homesales ) College tuition: National Center for Education Statistics (https://nces.ed.gov/fastfacts/display.asp?id=76 ) Weather temperature or precipitation: (http://www.weather.gov/help-past-weather ) Stock price: Yahoo Finance (https://finance.yahoo.com ) Main Post: Once you have historical data, address the following: 1. State the variable you are forecasting. 2. Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data values. 3. Use the Excel template (found in Unit 9 LiveBinder) to forecast the next time period using moving average, weighted moving average and exponential smoothing with your choice of alpha. 4. Copy/paste the results of each method. Be sure to state the number of periods used in the moving average method, the weights used in the weighted moving average, and the value of alpha used in exponential smoothing. 5. Clearly state the “next period” prediction for each method. ****************************************************************************************** I will use the National Association of Realtors Website (http://www.realtor.org/topics/existinghome-sales ) and I downloaded the “Single-Family Existing Home Sales and Prices” spreadsheet for Database work. 1. I will look at the (not-seasonally adjusted) median sale price for the West column over the past year by month (May 2015 – May 2016). Here is the data: Year 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May West 325,800 331,300 329,300 322,000 322,200 324,200 321,700 324,900 313,400 312,300 322,500 337,800 348,100 2&3) 3-Month Moving Average – forecast is $336,133.33 3-Month Weighted Moving Average – forecast is $340,400 Weights are 3 = most recent month, 2 = 1-month prior, 1 = 2-months prior Exponential Smoothing, alpha = 0.25 – forecast is $330,412.95
Number of Periods Averaged 4 Data Date Indicates which cells in column to the right need a formula Moving average forecast Period Number Data 1 50 2 55 3 53 4 60 5 58 Formula Needed --> 54,50000 6 75 Formula Needed --> 56,50000 7 63 Formula Needed --> 61,50000 8 71 Formula Needed --> 64,00000 9 76 Formula Needed --> 66,75000 10 80 Formula Needed --> 71,25000 Formula Needed --> 72,50000 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Analysis of Forecast Error MAD 8,08333 MSE 94,64583 MAPE 11,00830% Formulas absolute value of error error squared error percentage error Number of weighted periods Formula to copy 1 50,00000 2 52,50000 3 52,66667 3,50000 3,50000 12,25000 6,03% 4 54,50000 18,50000 18,50000 342,25000 24,67% 5 55,20000 1,50000 1,50000 2,25000 2,38% 6 58,50000 7,00000 7,00000 49,00000 9,86% 7 59,14286 9,25000 9,25000 85,56250 12,17% 8 60,62500 8,75000 8,75000 76,56250 10,94% 9 62,33333 10 64,10000 11 12 Color Key: Cells that require student input Excel Intermediate Calculations Excel Calculated Results Major Headings Minor Headings Reference/Check Points Number of Periods 4 Data Date Period number data Indicates which cells in column to the right need a formula weight 1 50 1 2 55 3 3 53 4 4 60 7 5 58 Formula Needed --> 6 75 Formula Needed --> 7 63 Formula Needed --> 8 71 Formula Needed --> 9 76 Formula Needed --> 10 80 Formula Needed --> 11 12 13 14 15 16 17 18 19 20 21 22 23 Formula Needed --> 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Analysis of Forecast Error MAD 7,23333 Weighted moving average forecast absolute value of error error MSE 79,21704 squared error MAPE 9,81425% percentage error 56,46667 1,53333 1,53333 2,35111 2,64% 57,33333 17,66667 17,66667 312,11111 23,56% 66,00000 -3,00000 3,00000 9,00000 4,76% 65,00000 6,00000 6,00000 36,00000 8,45% 68,80000 7,20000 7,20000 51,84000 9,47% 72,00000 8,00000 8,00000 64,00000 10,00% 76,00000 Color Key: Cells that require student input Excel Intermediate Calculations Formulas Excel Calculated Results Number of weighted periods Formula to copy 1 50,00000 2 53,75000 3 53,37500 4 56,46667 5 56,46667 6 56,46667 7 56,46667 8 56,46667 9 56,46667 10 56,46667 11 12 Major Headings Minor Headings Reference/Check Points Analysis of For Weight alpha MAD 0,3 7,20079 Data Date Period Number Exponential forecast data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 50 55 53 60 58 75 63 71 76 80 50,00000 50,00000 51,50000 51,95000 54,36500 55,45550 61,31885 61,82320 64,57624 68,00337 71,60236 error 0,00000 5,00000 1,50000 8,05000 3,63500 19,54450 1,68115 9,17681 11,42376 11,99663 absolute value of error 0,00000 5,00000 1,50000 8,05000 3,63500 19,54450 1,68115 9,17681 11,42376 11,99663 nalysis of Forecast Error MSE MAPE 84,87148 10,32850% squared error 0,00000 25,00000 2,25000 64,80250 13,21323 381,98748 2,82627 84,21375 130,50237 143,91924 absolute percentage error 0,00% 9,09% 2,83% 13,42% 6,27% 26,06% 2,67% 12,93% 15,03% 15,00%

Tutor Answer

Chucks574
School: UIUC

Thank you so much

Running head: BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS

BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS:
Name:
Institution affiliation:
Date:

1

BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS
1.

2

State the variable you are forecasting.

The variable that will be analyzed will be (not seasonally adjusted) Sales Price of
Existing Single-Family Homes in the US over a period of one year by month (OCT 2017- OCT
2018). The data was obtained from the National Association of realtors Website
(http://www.realtor.org/topics/existinghome-sales) and it was downloaded from the “SingleFamily Existing Home Sales and Prices” section.
2.

Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data

values.
Data
year
month
US
2017
October
4,880,000
2017
November 5,050,000
2017
December 4,950,000
2018
January
4,760,000
2018
February
4,960,000
2018
March
4,990,000
2018
April
4,840,000
2018
May
4,790,000
2018
June
4,760,000
2018
July
4,750,000
2018
August
4,740,000
2018
September 4,580,000
2018
October
4,620,000
3. Use the Time Series Forecasting Templates to forecast using moving average,
weighted moving average, and exponential smoothing.
Three month moving average- forecast is 4672500.00
Number
of
Periods
Average
d

4

Analysis of Forecast Error
MAD

MSE

MAPE

BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS

3

91666.6
6667

10062500000 1.9339
.00000
0%

Data
Period
Number

Data

Indicates
which cells
in column to
the right
need a
formula

Movin
g
averag
e
forecas
t

error

absolut
e value
of error

squared
error

percen
tage
error

1

488000
0
505000
0
495000
0
476000
0
496000
0
499000
0
484000
0

Formula
Needed -->
Formula
Needed -->
Formula
Needed -->

491000
0.00
493000
0.00
491500
0.00

50000.0
0
60000.0
0
75000.0
0

2500000000.
00
3600000000.
00
5625000000.
00

1.01%

8

479000
0

Formula
Needed -->

488750
0.00

97500.0
0

9506250000.
00

2.04%

9

476000
0

Formula
Needed -->

489500
0.00

135000.
00

18225000000 2.84%
.00

10

475000
0

...

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Review

Anonymous
Thanks, good work

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